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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Advancing State-of-the-Art Material Phase and Crystallographic Texture Characterization
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complicated systems such as post-consumer resins, property changes from physical or chemical processing, the addition of other measurement modalities and/or using more sophisticated machine learning techniques
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Methods for the Prediction and Correlation of Thermophysical Properties
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sustainable fuels to aid in their integration into the fuels market. Our group has state-of-the-art instrumentation to measure properties including the phase behavior, density, speed of sound, and thermal
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phone Edward Ryan Sisco [email protected] 301 975 2093 Description This opportunity focuses on the development of analytical methods and/or data processing techniques that could be used to
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well-structured form to the final industrial users. Recent advances in AI (Artificial Intelligence), specifically in NLP (Natural Language Processing) and ML (Machine Learning), are promising for
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research will involve the development of beam-forming elements (including lithographic antennas or silicon micromachined corrugated feedhorns), superconducting elements for coherent processing of the
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been applied to cells, tissues, and biomaterials. Our interest in further development is two-fold: (1) Numerical processing and analysis methods for extraction of actionable information from the rich
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protocols. NIST researchers, with support from the US Department of Energy's Advanced Research Projects Agency-Energy (ARPA-E), recently built a state-of-the-art Facility for Adsorbent Characterization
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example hardware based spiking neural networks, including the design, modeling, fabrication, and testing. Fabrication will be done in the NIST state-of-the-art clean room. Emphasis will be placed on